In 1985, the Commodore Amiga featured a GPU advanced for a personal computer at the time. It supported line draw, area fill, and included a type of stream processor called a blitter which accelerated the movement, manipulation and combination of multiple arbitrary bitmaps. Also included was a coprocessor with its own (primitive) instruction set capable of directly invoking a sequence of graphics operations without CPU intervention. Prior to this and for quite some time after, many other personal computer systems instead used their main, general-purpose CPU to handle almost every aspect of drawing the display, short of generating the final video signal.

In 1987, the IBM 8514 graphics system was released as one of the first video cards for IBM PC compatibles to implement fixed-function 2D primitives in electronic hardware. The same year, Sharp released the X68000, which used a custom graphics chipset[8] that was powerful for a home computer at the time, with a 65,536 color palette and hardware support for sprites, scrolling and multiple playfields,[9] eventually serving as a development machine for Capcom's CP System arcade board. Fujitsu later competed with the FM Towns computer, released in 1989 with support for a full 16,777,216 color palette.[10]

In 1991, S3 Graphics introduced the S3 86C911, which its designers named after the Porsche 911 as an implication of the performance increase it promised. The 86C911 spawned a host of imitators: by 1995, all major PC graphics chip makers had added 2D acceleration support to their chips. By this time, fixed-function Windows accelerators had surpassed expensive general-purpose graphics coprocessors in Windows performance, and these coprocessors faded away from the PC market.

In the PC world, notable failed first tries for low-cost 3D graphics chips were the S3ViRGE, ATIRage, and MatroxMystique. These chips were essentially previous-generation 2D accelerators with 3D features bolted on. Many were even pin-compatible with the earlier-generation chips for ease of implementation and minimal cost. Initially, performance 3D graphics were possible only with discrete boards dedicated to accelerating 3D functions (and lacking 2D GUI acceleration entirely) such as the PowerVR and the 3dfxVoodoo. However, as manufacturing technology continued to progress, video, 2D GUI acceleration and 3D functionality were all integrated into one chip. Rendition'sVerite chipsets were among the first to do this well enough to be worthy of note. In 1997, Rendition went a step further by collaborating with Hercules and Fujitsu on a "Thriller Conspiracy" project which combined a Fujitsu FXG-1 Pinolite geometry processor with a Vérité V2200 core to create a graphics card with a full T&L engine years before Nvidia's GeForce 256. This card, designed to reduce the load placed upon the system's CPU, never made it to market.

OpenGL appeared in the early '90s as a professional graphics API, but originally suffered from performance issues which allowed the Glide API to step in and become a dominant force on the PC in the late '90s.[19] However, these issues were quickly overcome and the Glide API fell by the wayside. Software implementations of OpenGL were common during this time, although the influence of OpenGL eventually led to widespread hardware support. Over time, a parity emerged between features offered in hardware and those offered in OpenGL. DirectX became popular among Windows game developers during the late 90s. Unlike OpenGL, Microsoft insisted on providing strict one-to-one support of hardware. The approach made DirectX less popular as a standalone graphics API initially, since many GPUs provided their own specific features, which existing OpenGL applications were already able to benefit from, leaving DirectX often one generation behind. (See: Comparison of OpenGL and Direct3D.)

Over time, Microsoft began to work more closely with hardware developers, and started to target the releases of DirectX to coincide with those of the supporting graphics hardware. Direct3D 5.0 was the first version of the burgeoning API to gain widespread adoption in the gaming market, and it competed directly with many more-hardware-specific, often proprietary graphics libraries, while OpenGL maintained a strong following. Direct3D 7.0 introduced support for hardware-accelerated transform and lighting (T&L) for Direct3D, while OpenGL had this capability already exposed from its inception. 3D accelerator cards moved beyond being just simple rasterizers to add another significant hardware stage to the 3D rendering pipeline. The NvidiaGeForce 256 (also known as NV10) was the first consumer-level card released on the market with hardware-accelerated T&L, while professional 3D cards already had this capability. Hardware transform and lighting, both already existing features of OpenGL, came to consumer-level hardware in the '90s and set the precedent for later pixel shader and vertex shader units which were far more flexible and programmable.

With the advent of the OpenGL API and similar functionality in DirectX, GPUs added shading to their capabilities. Each pixel could now be processed by a short program that could include additional image textures as inputs, and each geometric vertex could likewise be processed by a short program before it was projected onto the screen. Nvidia was first to produce a chip capable of programmable shading, the GeForce 3 (code named NV20). By October 2002, with the introduction of the ATIRadeon 9700 (also known as R300), the world's first Direct3D 9.0 accelerator, pixel and vertex shaders could implement looping and lengthy floating point math, and in general were quickly becoming as flexible as CPUs, and orders of magnitude faster for image-array operations. Pixel shading is often used for things like bump mapping, which adds texture, to make an object look shiny, dull, rough, or even round or extruded.[20]

With the introduction of the GeForce 8 series, which was produced by Nvidia, and then new generic stream processing unit GPUs became a more generalized computing device. Today, parallel GPUs have begun making computational inroads against the CPU, and a subfield of research, dubbed GPU Computing or GPGPU for General Purpose Computing on GPU, has found its way into fields as diverse as machine learning,[21]oil exploration, scientific image processing, linear algebra,[22]statistics,[23] 3D reconstruction and even stock options pricing determination. Over the years, the energy consumption of GPUs has increased and to manage it, several techniques have been proposed.[24]

Nvidia's CUDA platform was the earliest widely adopted programming model for GPU computing. More recently OpenCL has become broadly supported. OpenCL is an open standard defined by the Khronos Group which allows for the development of code for both GPUs and CPUs with an emphasis on portability.[25] OpenCL solutions are supported by Intel, AMD, Nvidia, and ARM, and according to a recent report by Evan's Data, OpenCL is the GPGPU development platform most widely used by developers in both the US and Asia Pacific.

Many companies have produced GPUs under a number of brand names. In 2009, Intel, Nvidia and AMD/ATI were the market share leaders, with 49.4%, 27.8% and 20.6% market share respectively. However, those numbers include Intel's integrated graphics solutions as GPUs. Not counting those numbers, Nvidia and ATI control nearly 100% of the market as of 2008.[26] In addition, S3 Graphics[27] (owned by VIA Technologies) and Matrox[28] produce GPUs.

In addition to the 3D hardware, today's GPUs include basic 2D acceleration and framebuffer capabilities (usually with a VGA compatibility mode). Newer cards like AMD/ATI HD5000-HD7000 even lack 2D acceleration; it has to be emulated by 3D hardware.

A dedicated GPU is not necessarily removable, nor does it necessarily interface with the motherboard in a standard fashion. The term "dedicated" refers to the fact that dedicated graphics cards have RAM that is dedicated to the card's use, not to the fact that most dedicated GPUs are removable. Dedicated GPUs for portable computers are most commonly interfaced through a non-standard and often proprietary slot due to size and weight constraints. Such ports may still be considered PCIe or AGP in terms of their logical host interface, even if they are not physically interchangeable with their counterparts.

Technologies such as SLI by Nvidia and CrossFire by AMD allow multiple GPUs to draw images simultaneously for a single screen, increasing the processing power available for graphics.

A motherboard with integrated graphics, which has HDMI, VGA and DVI outs.

Integrated graphics solutions, shared graphics solutions, or integrated graphics processors (IGP) utilize a portion of a computer's system RAM rather than dedicated graphics memory. IGPs can be integrated onto the motherboard as part of the chipset, or within the same die as CPU (like AMD APU or Intel HD Graphics). Some of AMD's IGPs use dedicated sideport memory on certain motherboards[clarification needed]. Computers with integrated graphics account for 90% of all PC shipments.[29][needs update] These solutions are less costly to implement than dedicated graphics solutions, but tend to be less capable. Historically, integrated solutions were often considered unfit to play 3D games or run graphically intensive programs but could run less intensive programs such as Adobe Flash. Examples of such IGPs would be offerings from SiS and VIA circa 2004.[30] However, modern integrated graphics processors such as AMD Accelerated Processing Unit and Intel HD Graphics are more than capable of handling 2D graphics or low stress 3D graphics.

As a GPU is extremely memory intensive, an integrated solution may find itself competing for the already relatively slow system RAM with the CPU, as it has minimal or no dedicated video memory. IGPs can have up to 29.856 GB/s of memory bandwidth from system RAM, however graphics cards can enjoy up to 264 GB/s of bandwidth over its memory-bus. [clarification needed] Older integrated graphics chipsets lacked hardware transform and lighting, but newer ones include it.[31][32][clarification needed]

This newer class of GPUs competes with integrated graphics in the low-end desktop and notebook markets. The most common implementations of this are ATI's HyperMemory and Nvidia's TurboCache.

Hybrid graphics cards are somewhat more expensive than integrated graphics, but much less expensive than dedicated graphics cards. These share memory with the system and have a small dedicated memory cache, to make up for the high latency of the system RAM. Technologies within PCI Express can make this possible. While these solutions are sometimes advertised as having as much as 768MB of RAM, this refers to how much can be shared with the system memory.

It is becoming increasingly common to use a general purpose graphics processing unit as a modified form of stream processor. This concept turns the massive computational power of a modern graphics accelerator's shader pipeline into general-purpose computing power, as opposed to being hard wired solely to do graphical operations. In certain applications requiring massive vector operations, this can yield several orders of magnitude higher performance than a conventional CPU. The two largest discrete (see "Dedicated graphics cards" above) GPU designers, ATI and Nvidia, are beginning to pursue this approach with an array of applications. Both Nvidia and ATI have teamed with Stanford University to create a GPU-based client for the Folding@home distributed computing project, for protein folding calculations. In certain circumstances the GPU calculates forty times faster than the conventional CPUs traditionally used by such applications.[33][34]

Furthermore, GPU-based high performance computers are starting to play a significant role in large-scale modelling. Three of the 10 most powerful supercomputers in the world take advantage of GPU acceleration.[35]

NVIDIA cards support API extensions to the C programming language such as CUDA ("Compute Unified Device Architecture") and OpenCL. CUDA is specifically for NVIDIA GPUs whilst OpenCL is designed to work across a multitude of architectures including GPU, CPU and DSP (using vendor specific SDKs). These technologies allow specified functions (kernels) from a normal C program to run on the GPU's stream processors. This makes C programs capable of taking advantage of a GPU's ability to operate on large matrices in parallel, while still making use of the CPU when appropriate. CUDA is also the first API to allow CPU-based applications to directly access the resources of a GPU for more general purpose computing without the limitations of using a graphics API.

Since 2005 there has been interest in using the performance offered by GPUs for evolutionary computation in general, and for accelerating the fitness evaluation in genetic programming in particular. Most approaches compile linear or tree programs on the host PC and transfer the executable to the GPU to be run. Typically the performance advantage is only obtained by running the single active program simultaneously on many example problems in parallel, using the GPU's SIMD architecture.[36][37] However, substantial acceleration can also be obtained by not compiling the programs, and instead transferring them to the GPU, to be interpreted there.[38][39] Acceleration can then be obtained by either interpreting multiple programs simultaneously, simultaneously running multiple example problems, or combinations of both. A modern GPU (e.g.8800 GTX or later) can readily simultaneously interpret hundreds of thousands of very small programs.

An external GPU is a graphics processor located outside of the housing of the computer. External Graphics Processors are often used with laptop computers. Laptops might have a substantial amount of RAM and a sufficiently powerful Central Processing Unit(CPU), but often lack a powerful graphics processor (and instead have a less powerful, but energy efficient on-board graphics chip). On-board graphics chips are often not powerful enough for playing the latest games, or for other tasks (video editing, ...).

Therefore it is desirable to be able to attach to some external PCIe bus of a notebook. That may be an x1 2.0 5Gbit/s expresscard or mPCIe (wifi) port or a 10Gbit/s/16Gbit/s Thunderbolt1/Thunderbolt2 port. Those ports being only available on certain candidate notebook systems.[40][41]

External GPU's have had little official vendor support. Promising solutions such as Silverstone T004 (aka ASUS XG2)[42] and MSI GUS-II[43] were never released to the general public. MSI's Gamedock [44] promising to deliver a full x16 external PCIe bus to a purpose built compact 13" MSI GS30 notebook. Lenovo and Magma partnering in Sep-2014 to deliver official Thunderbolt eGPU support.[45]

This has not stopped enthusiasts from creating their own DIY eGPU solutions.[46][47] expresscard/mPCIe eGPU adapters/enclosures are usually acquired from BPlus (PE4C, PE4L, PE4C),[48] or EXP GDC.[49] native Thunderbolt eGPU adaptere/enclosures acquired from One Stop Systems,[50] AKiTiO,[51] Sonnet (often rebadge as Other World Computing — OWC) and FirmTek.